Research on Monitoring and Disposal Theory of FaceTime and Other Network Fraud Calls and Messages
(China Mobile Communications Group Guangdong Co., Ltd., Guangzhou 510623, China)
Abstract
FaceTime and other encrypted network calling tools pose new fraud risks, and the traditional SIM card monitoring system lacks adaptability. Based on the "Five-in-One" anti-fraud practical experience of Guangdong Mobile, this study constructs a fraud monitoring and disposal technical framework for encrypted communication scenarios, analyzes core technical application logic such as DPI deep parsing, multi-source data fusion, and AI-assisted judgment, and verifies its effectiveness in reducing fraud-related numbers and intercepting fraud losses. Research shows that the system integrating multi-source network data and AI decision-making can achieve accurate identification and efficient disposal of fraudulent behaviors in encrypted communication scenarios, providing practical support for the improvement of network anti-fraud theory.
Keywords
encrypted network calling; telecom network fraud; five-in-one system; multi-source data fusion; AI-assisted judgment
References
- [1] Cui Ruiheng Research on the influencing factors of public willingness to continue using the National Anti Fraud Center APP [D]. Beijing: People's Public Security University of China, 2025
- [2] Xie Zhiwen The main characteristics, governance difficulties, and practices of video communication software fraud [J]. China Information Security, 2025 (7): 37-39
- [3] Xu Chenxia, Zhang Yibing, Chen Zhe Research on the Information and Communication Industry Supporting the Prevention, Crackdown and Governance of Telecommunications Network Fraud System [J]. Information and Communication Technology and Policy, 2022 (10): 75-78
- [4] China Academy of Information and Communications Technology White Paper on Prevention and Governance of Telecommunications Network Fraud in the Information and Communication Industry (2023) [R]. Beijing: China Academy of Information and Communications Technology, 2023
- [5] Chen Zihan, Cheng Guang, Xu Ziheng, etc Survey of Internet encrypted traffic detection, classification and identification [J]. Journal of Computer Science, 2023, 46 (5): 1060-1085
- [6] Wang Gang, Gao Yunpeng, Yang Songru, etc A review of research on encrypted malicious traffic detection methods based on deep learning [J]. Information Network Security, 2025, 25 (8): 1276-1301
- [7] Zhang Jiejun, Tang Yingchun, Ji Shuyun, etc A telecom fraud identification method based on graph neural network [J]. Electronic Technology Applications, 2021, 47 (6): 25-29, 34
- [8] Tong Xin, Yang Ying, Suo Qiwei, etc Overview of Encryption Traffic Analysis Methods Based on Machine Learning [J]. Integrated Technology, 2024, 13 (5): 74-92
- [9] Xu De Research and Design of Multi dimensional Anti Fraud System Based on AI [J]. Network Security Technology and Application, 2025 (8): 78-81
- [10] Li Zhenghua The current situation and response strategies of AI telecommunications network fraud crimes based on deep forgery technology [J]. Network Security Technology and Application, 2025 (7): 151-154
- [11] Chen Guorun, Mu Meirong, Zhang Rui, etc Implementation of a Communication Fraud Identification Model Based on Federated Learning [J]. Telecommunications Science, 2020, 36 (S1): 300-306
- [12] He Zeping, Xu Jian, Dai Hua, etc A Review of Application Technology Research in Federated Learning [J]. Information Network Security, 2024, 24 (12): 1831-1844
About the authors
Liang Meng (1981-), male, Han ethnicity, from Gaozhou, Guangdong, engineer, bachelor's degree, research direction: network big data, telecommunications network fraud governance; Yang Jinwen (1995-), female, Han nationality, from Jieyang, Guangdong, engineer, undergraduate, research direction: study of telecommunications network fraud methods; Lu Jianhong (1983-), male, Han ethnicity, from Foshan, Guangdong, engineer, bachelor's degree, research direction: research on telecommunications network fraud methods; Xu Min (1990-), female, Han ethnicity, from Chaozhou, Guangdong, is a senior engineer and master's student. Her research focuses on big data mining and anti fraud model construction for telecommunications security; Chen Zejun (1990-), female, Han ethnicity, from Shantou, Guangdong, is an engineer with a bachelor's degree. Her research direction is mobile communication network management and security.